{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from load_data import loaddata\n",
    "from turbine_dataset import TurbineDataset\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.optim import Adam\n",
    "from torch import nn\n",
    "from trainer import Trainer\n",
    "from model import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainrate = 0.8\n",
    "testrate = 0.1\n",
    "seq = 24\n",
    "device = \"cuda\"\n",
    "batch_size = 128\n",
    "hidden_size = 256\n",
    "output_size = 1\n",
    "num_layers = 3\n",
    "epoch = 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size, df = loaddata()\n",
    "traindf = df[:int(len(df) * trainrate)]\n",
    "testdf = df[-int(len(df) * testrate):]\n",
    "valdf = df[len(traindf):-len(testdf)]\n",
    "trainset = TurbineDataset(traindf, seq, device)\n",
    "valset = TurbineDataset(valdf, seq, device)\n",
    "testset = TurbineDataset(testdf, seq, device)\n",
    "trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True)\n",
    "valloader = DataLoader(valset, batch_size=batch_size)\n",
    "testloader = DataLoader(testset, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(features=input_size, hidden=hidden_size, output=output_size, length=1024, num_layers=num_layers).to(device=device)\n",
    "optimizer = Adam(model.parameters(), lr=0.0001)\n",
    "criterion = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Trainer(criterion, optimizer, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training 0th epoch, loss is 0.07391586006032648\n",
      "training 1th epoch, loss is 0.06877584319078812\n",
      "training 2th epoch, loss is 0.06805855532487233\n",
      "training 3th epoch, loss is 0.06748263391459154\n",
      "training 4th epoch, loss is 0.06712305363336647\n",
      "training 5th epoch, loss is 0.06678363782250218\n",
      "training 6th epoch, loss is 0.06646630611546614\n",
      "training 7th epoch, loss is 0.06622648882438187\n",
      "training 8th epoch, loss is 0.06590453420455257\n",
      "training 9th epoch, loss is 0.06577280018202684\n",
      "training 10th epoch, loss is 0.0653349877162664\n",
      "training 11th epoch, loss is 0.06465294534271514\n",
      "training 12th epoch, loss is 0.06408515441472884\n",
      "training 13th epoch, loss is 0.06370705076389843\n",
      "training 14th epoch, loss is 0.06330901886750427\n",
      "training 15th epoch, loss is 0.06306098421897602\n",
      "training 16th epoch, loss is 0.0626172545503963\n",
      "training 17th epoch, loss is 0.062198557908198344\n",
      "training 18th epoch, loss is 0.061960537867896535\n",
      "training 19th epoch, loss is 0.061502737958950025\n",
      "training 20th epoch, loss is 0.061253569858079705\n",
      "training 21th epoch, loss is 0.06087846338783425\n",
      "training 22th epoch, loss is 0.06061218866250581\n",
      "training 23th epoch, loss is 0.06033098310646084\n",
      "training 24th epoch, loss is 0.06003242025013875\n"
     ]
    }
   ],
   "source": [
    "trainer.train(trainloader, epoch=epoch)\n",
    "# model.load_state_dict(torch.load(\"model_50.pth\"))\n",
    "# torch.save(model.state_dict(), f\"model_{seq}_{epoch}.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17256\n",
      "17256\n"
     ]
    }
   ],
   "source": [
    "y_p, y_t = trainer.predict(testloader)\n",
    "print(len(y_p))\n",
    "print(len(y_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.036947924634685075\n",
      "0.14641733794124676\n"
     ]
    }
   ],
   "source": [
    "mseloss = 0\n",
    "maeloss = 0\n",
    "for t, p in zip(y_t, y_p):\n",
    "    mseloss += (t - p) ** 2\n",
    "    maeloss += abs(t - p)\n",
    "print(mseloss / len(y_t))\n",
    "print(maeloss / len(y_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17256\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设 predictions 和 actual_values 已经定义\n",
    "# predictions 是模型的预测值，actual_values 是真实值\n",
    "\n",
    "actual_values = y_t\n",
    "predictions = y_p\n",
    "print(len(actual_values))\n",
    "length = 1000\n",
    "actual_values = actual_values[:length]\n",
    "predictions = predictions[:length]\n",
    "\n",
    "# 创建散点图\n",
    "plt.scatter(actual_values, predictions, color='blue', label='Actual vs Predicted')\n",
    "\n",
    "# 添加标签和标题\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('Actual vs Predicted Values')\n",
    "\n",
    "# 添加对角线，用于比较完美预测的情况\n",
    "plt.plot([min(actual_values), max(actual_values)], [min(actual_values), max(actual_values)], linestyle='--', color='red', linewidth=2, label='Perfect Prediction')\n",
    "\n",
    "# 显示图例\n",
    "plt.legend()\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdc691935b0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "length = 50\n",
    "x = [i for i in range(length)]\n",
    "plt.plot(x, predictions[:length], color=\"red\")\n",
    "plt.plot(x, actual_values[:length])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter containing:\n",
      "tensor([[ 1.4618, -1.0051,  0.9058,  ...,  0.2008, -0.2485, -0.3372],\n",
      "        [-0.4206, -0.9641,  0.9606,  ..., -0.1510, -0.1707,  0.0030],\n",
      "        [ 1.4450, -0.3345, -1.0624,  ...,  0.0750, -0.8005, -0.2991],\n",
      "        ...,\n",
      "        [ 0.4270,  1.0527, -0.9752,  ...,  0.1229, -0.9844, -0.2560],\n",
      "        [ 1.5819, -2.4454,  0.9166,  ...,  1.9124, -2.6304, -1.5601],\n",
      "        [-0.1926, -1.3604, -1.5129,  ...,  0.5667,  0.5515,  0.3897]],\n",
      "       device='cuda:0', requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "w = model.codebook.embedding.weight\n",
    "print(w)"
   ]
  }
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